Content creators are increasingly creating visual artistic works in digital formats. Additionally, visual artistic works originally created with physical media are being converted to digital formats. In many scenarios, this content is contributed to or indexed for digital content repositories—making the content available to device users. As a result, the amount of artistic content available to users in digital form not only is staggeringly large, but also continues to grow. Given the amount of available content, users are unlikely to know about the entirety of this content. To this extent, search services and the content repositories themselves provide tools that allow users to search for content items having desired objects and/or visual characteristics.
Conventional techniques for searching image content involve matching text-based queries to tags (e.g., strings of text) that are associated with the images to describe the respective image content. These techniques return images having tags that match the text-based queries. Tagging can limit these techniques to a pre-defined taxonomy, however. Other conventional techniques for searching image content search according to a provided visual example. In these techniques, a user may select an image that is used as a basis for a search query. However, these techniques may simply search for near-duplicates of the selected query example (e.g., search for a similar digital photograph) or for particular instances of objects depicted in the scene (e.g., when the query example includes Notre-Dame Cathedral the search identifies other images that include Notre-Dame Cathedral). Users that search for images using such conventional techniques may not be shown images having characteristics that match the characteristics desired. Consequently, conventional image searching techniques may hamper the creation of visual artistic works.